Innovation by integration of Drum-Buffer-Rope (DBR) method with Scrum-Kanban and use of Monte Carlo simulation for maximizing throughput in agile project management
Bibliographic record
Abstract
Highly volatile, uncertain, complex and ambiguous environments (VUCA) complicate and condition project management. With the emergence of agile project management, it is proposed to co-construct it with the client's active participation. Two used agile methodologies are Scrum and Kanban. Scrum is based on executing fast, interactive cycles (Sprints) for the incremental construction of products. Kanban promotes the balance of the continuous workflow through synchronizing tasks and seeking perfection. The combined use of Scrum-Kanban facilitates the integration of the best of both approaches. The Theory of Constraints (TOC) proposes a method for managing constraints in a system (Constraint Management). The Drum-Buffer-Rope (DBR) method and Buffer Management are practical applications of this theory. This study seeks to maximize the continuous flow of value (Throughput) in agile project management by synergistically integrating the DBR method with Scrum-Kanban. The five-step process is implemented for the planning, executing, and controlling the Kanban board in a Scrum Sprint cycle. Four scenarios are evaluated: (1) Balanced Line; (2) Unbalanced Line; (3) Unbalanced Line Modification 1 - Stable, Robust and Fast; and (4) Unbalanced Line Modification 2 - Focusing and Elevation. Measurement of completed work (Kanban cards in the "Done" column) and final inventory for the Sprint cycle reveals that Simulation 4 is the optimal scenario, achieving the highest average "output" ("Done" cards) with reduced inventory ("Doing" cards). The integration of DBR with Scrum-Kanban maximizes the completed work (Throughput) and minimizes the final inventory of the Sprint cycle, which is corroborated by the principle of Little's Law.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".